Abstract
Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio frequency identification (RFID) applications. However, the existing estimation mechanisms are proposed for the static case where tag population remains constant, thus leaving the more challenging dynamic case unaddressed. This chapter introduces a generic framework of stable and accurate estimation schemes based on Kalman filter for both static and dynamic RFID systems. We first model the system dynamics as discrete stochastic processes and leverage the techniques in extended Kalman filter (EKF) and cumulative sum control chart (CUSUM) to estimate tag population for static/dynamic systems. By employing Lyapunov drift analysis, we characterise the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters. The relative estimation error is bounded and converged to zero at exponential rate.
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Notes
- 1.
The outputs of the hash function have a uniform distribution such that the tag can choose any slot within the round with the equal probability.
- 2.
For two variables X, Y, asymptotic notation X =  Θ(Y ) implies that there exist positives c 1, c 2 and x 0 such that for ∀X > x 0, it follows that c 1 X ≤ Y ≤ c 2 X.
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Yu, J., Chen, L. (2019). From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective. In: Tag Counting and Monitoring in Large-Scale RFID Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-91992-8_3
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DOI: https://doi.org/10.1007/978-3-319-91992-8_3
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